294 citations · 1.2k across the 73 of their papers we have counts for
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Partial Identifiability in Discrete Data With Measurement Error
Noam Finkelstein, Roy Adams, Suchi Saria +1
When data contains measurement errors, it is necessary to make assumptions relating the observed, erroneous data to the unobserved true phenomena of interest. These assumptions sho…
Differentiable Causal Discovery Under Unmeasured Confounding
Rohit Bhattacharya, Tushar Nagarajan, Daniel Malinsky +1
The data drawn from biological, economic, and social systems are often confounded due to the presence of unmeasured variables. Prior work in causal discovery has focused on discret…
Path Dependent Structural Equation Models
Ranjani Srinivasan, Jaron Lee, Rohit Bhattacharya +2
Causal analyses of longitudinal data generally assume that the qualitative causal structure relating variables remains invariant over time. In structured systems that transition be…
An Interventionist Approach to Mediation Analysis
James M. Robins, Thomas S. Richardson, Ilya Shpitser
Judea Pearl's insight that, when errors are assumed independent, the Pure (aka Natural) Direct Effect (PDE) is non-parametrically identified via the Mediation Formula was `path-bre…
Multivariate Counterfactual Systems And Causal Graphical Models
Ilya Shpitser, Thomas S. Richardson, James M. Robins
Among Judea Pearl's many contributions to Causality and Statistics, the graphical d-separation} criterion, the do-calculus and the mediation formula stand out. In this chapter we s…
Deriving Bounds and Inequality Constraints Using LogicalRelations Among Counterfactuals
Noam Finkelstein, Ilya Shpitser
Causal parameters may not be point identified in the presence of unobserved confounding. However, information about non-identified parameters, in the form of bounds, may still be r…